Functional Connectivity and Baseline Networks of the White Matter Brain: Development and Dissemination of Algorithms and Tools

白质脑的功能连接和基线网络：算法和工具的开发和传播

• 批准号: 10548825
• 负责人: Bharat Bhusan Biswal
• 金额: $ 53.16万
• 依托单位: NEW JERSEY INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-10 至 2025-10-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10548825
• 关键词: Address; Adult; Affect; Affective; Age; Algorithms; Anesthesia procedures; Applications Grants; Area; Atlases; Brain; Brain Diseases; Brain region; Characteristics; Clinical; Cognitive; Communities; Comprehension; Corpus Callosum; Data; Data Set; Development; Diagnosis; Diagnostic; Diffusion Magnetic Resonance Imaging; Dimensions; Disease; Disease Progression; Foundations; Functional Magnetic Resonance Imaging; Future; Gender; Goals; Graph; Growth; Human; Individual; Intelligence; Investigation; Knowledge; Light; Liquid substance; Magnetic Resonance Imaging; Maps; Measures; Mental disorders; Methods; Mining; Monitor; Motor; Neurologic; Noise; Phenotype; Population; Process; Property; Reporting; Reproducibility; Rest; Sensory; Series; Short-Term Memory; Signal Transduction; Sleep; Statistical Models; Systems Integration; Testing; Time; United States National Institutes of Health; Validity and Reliability; Vocabulary; Work; advanced analytics; age group; analytical tool; blood oxygen level dependent; brain volume; cognitive function; cohort; connectome; experience; gray matter; imaging study; improved; machine learning method; neglect; neuroimaging; neuropsychiatric disorder; response; secondary analysis; tool; trait; transmission process; vector; white matter

PROJECT ABSTRACT The discovery of functional brain connectivity (FC) and functional networks (FNs) have propelled the neuroimaging field, particularly in functional magnetic resonance imaging (fMRI), which has experienced an exponential growth in recent years. FNs have allowed us to better understand extrinsic and intrinsic brain properties in various disease and healthy states, leading to better characterization of neuropsychiatric disorders. However, current fMRI analyses are constrained to the gray matter (GM) region of the brain and fMRI data from the white matter (WM) region are often discarded, which makes up approximately 50% of the brain by volume. Many brain disorders have been associated with WM deficiencies, since WM is critical for the transmission of information to the GM cortical areas. Despite findings of blood-oxygen-level-dependent (BOLD) signals in the WM, WM-FNs are yet to be fully characterized, and neither the mechanism by which WM-FNs may affect GM-FNs, nor how WM-FNs are associated with phenotypic traits are known. The long-term goal of this project is to better understand the effect of WM-FNs on normal cognitive functions of the human brain and apply fMRI data from various healthy and diseased populations for more reliable diagnostics and monitoring. The rationale for this study is based on our preliminary studies which investigated WM-FNs using the Human Connectome Project dataset. We found that WM-FNs are correlated with subregions of the corpus callosum, a critical WM region relaying information between the two cortical hemispheres. Furthermore, we determined an overlap between the WM-FNs and tracts from diffusion tensor imaging (DTI). In this study we will examine WM-FNs of the whole brain using resting fMRI data from two large independent cohorts. We hypothesize that the FN measures derived from WM will be similar to that of GM and the metrics can be used to reliably predict phenotypic traits. The hypothesis will be tested with the following specific aims: Aim1: To develop and evaluate the time-series, FC and FN characteristics of WM of the whole - brain; Aim 2: To investigate WM-phenotype associations and the predictability of phenotypes using WM-FNs; and Aim 3: To develop and disseminate a WM-FN toolbox. To the best of our knowledge, this study will be the first to examine the reliability and validity of WM-FNs in resting fMRI data, and its relation to brain function. The proposed work holds significant contribution since it will facilitate the use of WM-FN methods for the neuroimaging community, which currently lacks the necessary analytic tools to reliably characterize WM function. This study will provide a strong foundation f or future clinical use of both WM-FNs and GM-FNs, to understand brain function more comprehensively, in addition to facilitating the use of reliable and reproducible WM-FC methods.

项目摘要 功能性大脑连接性（FC）和功能网络（FNS）的发现已推动 神经影像领域，特别是在功能磁共振成像（fMRI）中，它已经经历了 近年来指数增长。 FNS使我们能够更好地理解外在和内在的大脑 各种疾病和健康状态的特性，从而更好地表征神经精神病学 疾病。但是，当前的功能磁共振成像分析受到大脑的灰质（GM）区域的约束 来自白质（WM）区域的fMRI数据通常被丢弃，这约占该区域的50％ 大脑按体积。许多脑部疾病与WM缺陷有关，因为WM对 信息传输到通用汽车皮质区域。尽管发现血氧水平依赖性 （BOLD）WM中的信号，WM-FN尚未充分表征，也不是其机制 WM-FNS可能会影响GM-FN，也可能已知WM-FN与表型性状相关。 该项目的长期目标是更好地了解WM-FN对正常认知的影响 人脑的功能并应用来自各种健康和患病人群的fMRI数据以获取更多 可靠的诊断和监测。这项研究的理由是基于我们的初步研究 使用人类Connectome项目数据集研究了WM-FN。我们发现WM-FNS相关 与call体的子区域相关，这是两个皮质之间的关键WM区域中继信息 半球。此外，我们确定了从扩散张量的WM-FN和区域之间的重叠 成像（DTI）。在这项研究中，我们将使用来自两个大型的静止fMRI数据检查整个大脑的WM-FNS 独立人群。我们假设从WM得出的FN措施将与GM相似，并且 这些指标可用于可靠地预测表型特征。该假设将通过以下检验 具体目的：AIM1：开发和评估整个WM的时间序列，FC和FN特征 - 脑;目的2：研究WM-表型关联和使用WM-FNS的表型的可预测性； 目标3：开发和传播WM-FN工具箱。据我们所知，这项研究将是 首先检查WM-FN在静止fMRI数据中的可靠性和有效性及其与大脑功能的关系。这 拟议的工作具有重大贡献，因为它将促进使用WM-FN方法 神经影像社区目前缺乏必要的分析工具来可靠地表征WM 功能。这项研究将为WM-FNS和GM-FNS提供强大的基础或将来的临床使用，以便 除了促进可靠且可重复的使用外，更全面地了解大脑功能 WM-FC方法。